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Guessing and Forgetting: A Latent Class Model for Measuring Learning

Published online by Cambridge University Press:  04 January 2017

M. Ken Cor and
Gaurav Sood
M. Ken Cor*
Affiliation:
Faculty of Pharmacy and Pharmaceutical Sciences, 3-209 Edmonton Clinic Health Academy, University of Alberta, Edmonton, CA T5M1B3
Gaurav Sood
Affiliation:
e-mail: gsood07@gmail.com
*
e-mail: mcor@ualberta.ca
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Abstract

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Guessing on closed-ended knowledge items is common. Under likely-to-hold assumptions, in the presence of guessing, the most common estimator of learning, difference between pre- and postprocess scores, is negatively biased. To account for guessing-related error, we develop a latent class model of how people respond to knowledge questions and identify the model with the mild assumption that people do not lose knowledge over short periods of time. A Monte Carlo simulation over a broad range of informative processes and knowledge items shows that the simple difference score is negatively biased and the method we develop here is unbiased. To demonstrate its use, we apply our model to data from Deliberative Polls. We find that estimates of learning, once adjusted for guessing, are about 13% higher. Adjusting for guessing also eliminates the gender gap in learning, and halves the pre-deliberation gender gap on political knowledge.

Information

Type
Articles
Information
Political Analysis , Volume 24 , Issue 2 , Spring 2016, pp. 226 - 242
Copyright
Copyright © The Author 2016. Published by Oxford University Press on behalf of the Society for Political Methodology 

Footnotes

Author's note: We thank Ed Haertel for his advice and encouragement, Robert Luskin for improving some of the language, and Pablo Barberá, Ying Cui, Kabir Khanna, Marc Meredith, and Pete Mohanty for useful comments. Replication files for this study are available on the Harvard Dataverse at http://dx.doi.org/10.7910/DVN/HZHVCU. See Cor and Sood (2016). Supplementary materials for this article are available on the Political Analysis Web site.

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